{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection for Uplift Modeling\n",
    "  \n",
    "    \n",
    "This notebook includes two sections:  \n",
    "- **Feature selection**: demonstrate how to use Filter methods to select the most important numeric features\n",
    "- **Performance evaluation**: evaluate the AUUC performance with top features dataset\n",
    "  \n",
    "*(Paper reference: [Zhao, Zhenyu, et al. \"Feature Selection Methods for Uplift Modeling.\" arXiv preprint arXiv:2005.03447 (2020).](https://arxiv.org/abs/2005.03447))*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:16.975121Z",
     "start_time": "2021-11-29T22:45:16.749881Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:18.094794Z",
     "start_time": "2021-11-29T22:45:16.976822Z"
    }
   },
   "outputs": [],
   "source": [
    "from causalml.dataset import make_uplift_classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import FilterSelect class for Filter methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:18.103040Z",
     "start_time": "2021-11-29T22:45:18.097362Z"
    }
   },
   "outputs": [],
   "source": [
    "from causalml.feature_selection.filters import FilterSelect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:18.109523Z",
     "start_time": "2021-11-29T22:45:18.105177Z"
    }
   },
   "outputs": [],
   "source": [
    "from causalml.inference.tree import UpliftRandomForestClassifier\n",
    "from causalml.inference.meta import BaseXRegressor, BaseRRegressor, BaseSRegressor, BaseTRegressor\n",
    "from causalml.metrics import plot_gain, auuc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:18.112934Z",
     "start_time": "2021-11-29T22:45:18.111213Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:18.116303Z",
     "start_time": "2021-11-29T22:45:18.114315Z"
    }
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "logger = logging.getLogger('causalml')\n",
    "logging.basicConfig(level=logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate dataset\n",
    "\n",
    "Generate synthetic data using the built-in function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:18.120534Z",
     "start_time": "2021-11-29T22:45:18.117882Z"
    }
   },
   "outputs": [],
   "source": [
    "# define parameters for simulation\n",
    "\n",
    "y_name = 'conversion'\n",
    "treatment_group_keys = ['control', 'treatment1']\n",
    "n = 100000\n",
    "n_classification_features = 50\n",
    "n_classification_informative = 10\n",
    "n_classification_repeated = 0\n",
    "n_uplift_increase_dict = {'treatment1': 8}\n",
    "n_uplift_decrease_dict = {'treatment1': 4}\n",
    "delta_uplift_increase_dict = {'treatment1': 0.1}\n",
    "delta_uplift_decrease_dict = {'treatment1': -0.1}\n",
    "\n",
    "random_seed = 20200808"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:19.388449Z",
     "start_time": "2021-11-29T22:45:18.123271Z"
    }
   },
   "outputs": [],
   "source": [
    "df, X_names = make_uplift_classification(\n",
    "    treatment_name=treatment_group_keys,\n",
    "    y_name=y_name,\n",
    "    n_samples=n,\n",
    "    n_classification_features=n_classification_features,\n",
    "    n_classification_informative=n_classification_informative,\n",
    "    n_classification_repeated=n_classification_repeated,\n",
    "    n_uplift_increase_dict=n_uplift_increase_dict,\n",
    "    n_uplift_decrease_dict=n_uplift_decrease_dict,\n",
    "    delta_uplift_increase_dict = delta_uplift_increase_dict, \n",
    "    delta_uplift_decrease_dict = delta_uplift_decrease_dict,\n",
    "    random_seed=random_seed\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:19.417996Z",
     "start_time": "2021-11-29T22:45:19.391936Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>treatment_group_key</th>\n",
       "      <th>x1_informative</th>\n",
       "      <th>x2_informative</th>\n",
       "      <th>x3_informative</th>\n",
       "      <th>x4_informative</th>\n",
       "      <th>x5_informative</th>\n",
       "      <th>x6_informative</th>\n",
       "      <th>x7_informative</th>\n",
       "      <th>x8_informative</th>\n",
       "      <th>x9_informative</th>\n",
       "      <th>...</th>\n",
       "      <th>x56_uplift_increase</th>\n",
       "      <th>x57_uplift_increase</th>\n",
       "      <th>x58_uplift_increase</th>\n",
       "      <th>x59_increase_mix</th>\n",
       "      <th>x60_uplift_decrease</th>\n",
       "      <th>x61_uplift_decrease</th>\n",
       "      <th>x62_uplift_decrease</th>\n",
       "      <th>x63_uplift_decrease</th>\n",
       "      <th>conversion</th>\n",
       "      <th>treatment_effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>control</td>\n",
       "      <td>0.653960</td>\n",
       "      <td>-0.217603</td>\n",
       "      <td>1.856916</td>\n",
       "      <td>-0.075662</td>\n",
       "      <td>0.080971</td>\n",
       "      <td>-0.338374</td>\n",
       "      <td>-1.011470</td>\n",
       "      <td>0.528000</td>\n",
       "      <td>0.115418</td>\n",
       "      <td>...</td>\n",
       "      <td>1.533832</td>\n",
       "      <td>-2.183001</td>\n",
       "      <td>1.839608</td>\n",
       "      <td>0.755302</td>\n",
       "      <td>1.835047</td>\n",
       "      <td>-0.458431</td>\n",
       "      <td>-1.927525</td>\n",
       "      <td>2.765331</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>control</td>\n",
       "      <td>3.439658</td>\n",
       "      <td>0.477855</td>\n",
       "      <td>-0.377658</td>\n",
       "      <td>-1.317121</td>\n",
       "      <td>0.861815</td>\n",
       "      <td>-0.393180</td>\n",
       "      <td>0.503727</td>\n",
       "      <td>2.323846</td>\n",
       "      <td>1.229948</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.192333</td>\n",
       "      <td>-1.581815</td>\n",
       "      <td>2.423700</td>\n",
       "      <td>2.396904</td>\n",
       "      <td>0.296043</td>\n",
       "      <td>-1.961940</td>\n",
       "      <td>-1.444725</td>\n",
       "      <td>1.469213</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>treatment1</td>\n",
       "      <td>0.130907</td>\n",
       "      <td>-0.333536</td>\n",
       "      <td>0.474847</td>\n",
       "      <td>-0.352067</td>\n",
       "      <td>-0.024502</td>\n",
       "      <td>1.437105</td>\n",
       "      <td>0.566178</td>\n",
       "      <td>-0.232508</td>\n",
       "      <td>0.866236</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.301982</td>\n",
       "      <td>-0.933816</td>\n",
       "      <td>0.475274</td>\n",
       "      <td>1.540994</td>\n",
       "      <td>0.698066</td>\n",
       "      <td>0.545091</td>\n",
       "      <td>-0.084405</td>\n",
       "      <td>-2.337347</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>treatment1</td>\n",
       "      <td>-2.156683</td>\n",
       "      <td>1.120198</td>\n",
       "      <td>0.174293</td>\n",
       "      <td>-1.741426</td>\n",
       "      <td>0.488993</td>\n",
       "      <td>0.638340</td>\n",
       "      <td>-0.721928</td>\n",
       "      <td>1.802134</td>\n",
       "      <td>1.097178</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.129098</td>\n",
       "      <td>-1.183581</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>1.105735</td>\n",
       "      <td>-0.629281</td>\n",
       "      <td>-0.737041</td>\n",
       "      <td>-1.525081</td>\n",
       "      <td>1.416042</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>control</td>\n",
       "      <td>-2.708572</td>\n",
       "      <td>-0.799698</td>\n",
       "      <td>-2.199595</td>\n",
       "      <td>0.574077</td>\n",
       "      <td>0.083142</td>\n",
       "      <td>-0.389140</td>\n",
       "      <td>1.492101</td>\n",
       "      <td>1.725202</td>\n",
       "      <td>1.194315</td>\n",
       "      <td>...</td>\n",
       "      <td>1.582041</td>\n",
       "      <td>-1.176077</td>\n",
       "      <td>1.686322</td>\n",
       "      <td>0.480035</td>\n",
       "      <td>1.780710</td>\n",
       "      <td>0.862094</td>\n",
       "      <td>0.128872</td>\n",
       "      <td>-2.851344</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  treatment_group_key  x1_informative  x2_informative  x3_informative  \\\n",
       "0             control        0.653960       -0.217603        1.856916   \n",
       "1             control        3.439658        0.477855       -0.377658   \n",
       "2          treatment1        0.130907       -0.333536        0.474847   \n",
       "3          treatment1       -2.156683        1.120198        0.174293   \n",
       "4             control       -2.708572       -0.799698       -2.199595   \n",
       "\n",
       "   x4_informative  x5_informative  x6_informative  x7_informative  \\\n",
       "0       -0.075662        0.080971       -0.338374       -1.011470   \n",
       "1       -1.317121        0.861815       -0.393180        0.503727   \n",
       "2       -0.352067       -0.024502        1.437105        0.566178   \n",
       "3       -1.741426        0.488993        0.638340       -0.721928   \n",
       "4        0.574077        0.083142       -0.389140        1.492101   \n",
       "\n",
       "   x8_informative  x9_informative  ...  x56_uplift_increase  \\\n",
       "0        0.528000        0.115418  ...             1.533832   \n",
       "1        2.323846        1.229948  ...            -1.192333   \n",
       "2       -0.232508        0.866236  ...            -0.301982   \n",
       "3        1.802134        1.097178  ...            -2.129098   \n",
       "4        1.725202        1.194315  ...             1.582041   \n",
       "\n",
       "   x57_uplift_increase  x58_uplift_increase  x59_increase_mix  \\\n",
       "0            -2.183001             1.839608          0.755302   \n",
       "1            -1.581815             2.423700          2.396904   \n",
       "2            -0.933816             0.475274          1.540994   \n",
       "3            -1.183581             0.000318          1.105735   \n",
       "4            -1.176077             1.686322          0.480035   \n",
       "\n",
       "   x60_uplift_decrease  x61_uplift_decrease  x62_uplift_decrease  \\\n",
       "0             1.835047            -0.458431            -1.927525   \n",
       "1             0.296043            -1.961940            -1.444725   \n",
       "2             0.698066             0.545091            -0.084405   \n",
       "3            -0.629281            -0.737041            -1.525081   \n",
       "4             1.780710             0.862094             0.128872   \n",
       "\n",
       "   x63_uplift_decrease  conversion  treatment_effect  \n",
       "0             2.765331           0                 0  \n",
       "1             1.469213           1                 0  \n",
       "2            -2.337347           1                 0  \n",
       "3             1.416042           0                 0  \n",
       "4            -2.851344           0                 0  \n",
       "\n",
       "[5 rows x 66 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:19.608438Z",
     "start_time": "2021-11-29T22:45:19.419633Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>conversion</th>\n",
       "      <th>conversion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment_group_key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>0.499050</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment1</th>\n",
       "      <td>0.599680</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.549365</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          mean       size\n",
       "                    conversion conversion\n",
       "treatment_group_key                      \n",
       "control               0.499050     100000\n",
       "treatment1            0.599680     100000\n",
       "All                   0.549365     200000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the conversion rate and sample size in each group\n",
    "df.pivot_table(values='conversion',\n",
    "               index='treatment_group_key',\n",
    "               aggfunc=[np.mean, np.size],\n",
    "               margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:19.613010Z",
     "start_time": "2021-11-29T22:45:19.609718Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['x1_informative',\n",
       " 'x2_informative',\n",
       " 'x3_informative',\n",
       " 'x4_informative',\n",
       " 'x5_informative',\n",
       " 'x6_informative',\n",
       " 'x7_informative',\n",
       " 'x8_informative',\n",
       " 'x9_informative',\n",
       " 'x10_informative',\n",
       " 'x11_irrelevant',\n",
       " 'x12_irrelevant',\n",
       " 'x13_irrelevant',\n",
       " 'x14_irrelevant',\n",
       " 'x15_irrelevant',\n",
       " 'x16_irrelevant',\n",
       " 'x17_irrelevant',\n",
       " 'x18_irrelevant',\n",
       " 'x19_irrelevant',\n",
       " 'x20_irrelevant',\n",
       " 'x21_irrelevant',\n",
       " 'x22_irrelevant',\n",
       " 'x23_irrelevant',\n",
       " 'x24_irrelevant',\n",
       " 'x25_irrelevant',\n",
       " 'x26_irrelevant',\n",
       " 'x27_irrelevant',\n",
       " 'x28_irrelevant',\n",
       " 'x29_irrelevant',\n",
       " 'x30_irrelevant',\n",
       " 'x31_irrelevant',\n",
       " 'x32_irrelevant',\n",
       " 'x33_irrelevant',\n",
       " 'x34_irrelevant',\n",
       " 'x35_irrelevant',\n",
       " 'x36_irrelevant',\n",
       " 'x37_irrelevant',\n",
       " 'x38_irrelevant',\n",
       " 'x39_irrelevant',\n",
       " 'x40_irrelevant',\n",
       " 'x41_irrelevant',\n",
       " 'x42_irrelevant',\n",
       " 'x43_irrelevant',\n",
       " 'x44_irrelevant',\n",
       " 'x45_irrelevant',\n",
       " 'x46_irrelevant',\n",
       " 'x47_irrelevant',\n",
       " 'x48_irrelevant',\n",
       " 'x49_irrelevant',\n",
       " 'x50_irrelevant',\n",
       " 'x51_uplift_increase',\n",
       " 'x52_uplift_increase',\n",
       " 'x53_uplift_increase',\n",
       " 'x54_uplift_increase',\n",
       " 'x55_uplift_increase',\n",
       " 'x56_uplift_increase',\n",
       " 'x57_uplift_increase',\n",
       " 'x58_uplift_increase',\n",
       " 'x59_increase_mix',\n",
       " 'x60_uplift_decrease',\n",
       " 'x61_uplift_decrease',\n",
       " 'x62_uplift_decrease',\n",
       " 'x63_uplift_decrease']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature selection with Filter methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### method = F (F statistics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:19.616362Z",
     "start_time": "2021-11-29T22:45:19.614534Z"
    }
   },
   "outputs": [],
   "source": [
    "filter_f = FilterSelect() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:22.010814Z",
     "start_time": "2021-11-29T22:45:19.617574Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      method              feature  rank        score        p_value  \\\n",
      "0   F filter  x57_uplift_increase   1.0  1973.380496   0.000000e+00   \n",
      "0   F filter  x51_uplift_increase   2.0  1885.342364   0.000000e+00   \n",
      "0   F filter  x54_uplift_increase   3.0  1496.254091   0.000000e+00   \n",
      "0   F filter  x58_uplift_increase   4.0  1269.167710  4.224019e-277   \n",
      "0   F filter       x9_informative   5.0   677.066204  5.151887e-149   \n",
      "..       ...                  ...   ...          ...            ...   \n",
      "0   F filter       x30_irrelevant  59.0     0.004151   9.486301e-01   \n",
      "0   F filter       x50_irrelevant  60.0     0.001379   9.703808e-01   \n",
      "0   F filter       x36_irrelevant  61.0     0.001062   9.740069e-01   \n",
      "0   F filter       x6_informative  62.0     0.000428   9.834997e-01   \n",
      "0   F filter       x5_informative  63.0     0.000076   9.930457e-01   \n",
      "\n",
      "                               misc  \n",
      "0   df_num: 1.0, df_denom: 199996.0  \n",
      "0   df_num: 1.0, df_denom: 199996.0  \n",
      "0   df_num: 1.0, df_denom: 199996.0  \n",
      "0   df_num: 1.0, df_denom: 199996.0  \n",
      "0   df_num: 1.0, df_denom: 199996.0  \n",
      "..                              ...  \n",
      "0   df_num: 1.0, df_denom: 199996.0  \n",
      "0   df_num: 1.0, df_denom: 199996.0  \n",
      "0   df_num: 1.0, df_denom: 199996.0  \n",
      "0   df_num: 1.0, df_denom: 199996.0  \n",
      "0   df_num: 1.0, df_denom: 199996.0  \n",
      "\n",
      "[63 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "method = 'F'\n",
    "f_imp = filter_f.get_importance(df, X_names, y_name, method, \n",
    "                      treatment_group = 'treatment1')\n",
    "print(f_imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### method = LR (likelihood ratio test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:33.978540Z",
     "start_time": "2021-11-29T22:45:22.012629Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683141\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683138\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683140\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683138\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683140\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683141\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683141\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683138\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683140\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683135\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.570527\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.568449\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683141\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683141\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683138\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683127\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683141\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683140\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683134\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683140\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683136\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683136\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683136\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683129\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683137\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683135\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683138\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683138\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683137\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683131\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683141\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683140\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683136\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683125\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683138\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683136\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683136\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683141\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683139\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683137\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683135\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683139\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683137\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683135\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683138\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683138\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683141\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683140\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683136\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683126\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683140\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683135\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683140\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683133\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683141\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683141\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683140\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683141\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683139\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683126\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683113\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683138\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683134\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.678404\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.673481\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683139\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683139\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683136\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.678797\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.674807\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683141\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683139\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.678449\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.673272\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.679964\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.676614\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683140\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683142\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683134\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683128\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683137\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683143\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.683120\n",
      "         Iterations 4\n",
      "       method              feature  rank        score   p_value   misc\n",
      "0   LR filter  x57_uplift_increase   1.0  2070.582853  0.000000  df: 1\n",
      "0   LR filter  x51_uplift_increase   2.0  1969.081668  0.000000  df: 1\n",
      "0   LR filter  x54_uplift_increase   3.0  1596.059562  0.000000  df: 1\n",
      "0   LR filter  x58_uplift_increase   4.0  1339.970602  0.000000  df: 1\n",
      "0   LR filter       x9_informative   5.0   830.925812  0.000000  df: 1\n",
      "..        ...                  ...   ...          ...       ...    ...\n",
      "0   LR filter       x36_irrelevant  59.0     0.002396  0.960964  df: 1\n",
      "0   LR filter       x50_irrelevant  60.0     0.001808  0.966086  df: 1\n",
      "0   LR filter       x30_irrelevant  61.0     0.000956  0.975339  df: 1\n",
      "0   LR filter       x6_informative  62.0     0.000732  0.978420  df: 1\n",
      "0   LR filter       x5_informative  63.0     0.000029  0.995726  df: 1\n",
      "\n",
      "[63 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "method = 'LR'\n",
    "lr_imp = filter_f.get_importance(df, X_names, y_name, method, \n",
    "                      treatment_group = 'treatment1')\n",
    "print(lr_imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### method = KL (KL divergence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:46.414304Z",
     "start_time": "2021-11-29T22:45:33.981158Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       method              feature  rank     score p_value                misc\n",
      "0   KL filter  x51_uplift_increase   1.0  0.026008    None  number_of_bins: 10\n",
      "0   KL filter  x57_uplift_increase   2.0  0.023749    None  number_of_bins: 10\n",
      "0   KL filter       x9_informative   3.0  0.020550    None  number_of_bins: 10\n",
      "0   KL filter  x54_uplift_increase   4.0  0.018411    None  number_of_bins: 10\n",
      "0   KL filter  x58_uplift_increase   5.0  0.014443    None  number_of_bins: 10\n",
      "..        ...                  ...   ...       ...     ...                 ...\n",
      "0   KL filter       x29_irrelevant  59.0  0.000021    None  number_of_bins: 10\n",
      "0   KL filter       x30_irrelevant  60.0  0.000020    None  number_of_bins: 10\n",
      "0   KL filter       x17_irrelevant  61.0  0.000017    None  number_of_bins: 10\n",
      "0   KL filter       x45_irrelevant  62.0  0.000013    None  number_of_bins: 10\n",
      "0   KL filter       x7_informative  63.0  0.000011    None  number_of_bins: 10\n",
      "\n",
      "[63 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "method = 'KL'\n",
    "kl_imp = filter_f.get_importance(df, X_names, y_name, method, \n",
    "                      treatment_group = 'treatment1',\n",
    "                      n_bins=10)\n",
    "print(kl_imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found all these 3 filter methods were able to rank most of the **informative** and **uplift increase** features on the top."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance evaluation  \n",
    "\n",
    "Evaluate the AUUC (Area Under the Uplift Curve) score with several uplift models when using top features dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:46.497534Z",
     "start_time": "2021-11-29T22:45:46.415884Z"
    }
   },
   "outputs": [],
   "source": [
    "# train test split\n",
    "df_train, df_test = train_test_split(df, test_size=0.2, random_state=111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:46.510551Z",
     "start_time": "2021-11-29T22:45:46.499845Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 0 0 1 0 1 1 0]\n",
      "79114     treatment1\n",
      "76043        control\n",
      "47617        control\n",
      "53169        control\n",
      "175702       control\n",
      "111635    treatment1\n",
      "129212       control\n",
      "19247     treatment1\n",
      "49272     treatment1\n",
      "199314       control\n",
      "Name: treatment_group_key, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# convert treatment column to 1 (treatment1) and 0 (control)\n",
    "treatments = np.where((df_test['treatment_group_key']=='treatment1'), 1, 0)\n",
    "print(treatments[:10])\n",
    "print(df_test['treatment_group_key'][:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uplift RandomForest Classfier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:45:46.514976Z",
     "start_time": "2021-11-29T22:45:46.512507Z"
    }
   },
   "outputs": [],
   "source": [
    "uplift_model = UpliftRandomForestClassifier(control_name='control', max_depth=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:48:04.459656Z",
     "start_time": "2021-11-29T22:45:46.516815Z"
    }
   },
   "outputs": [],
   "source": [
    "# using all features\n",
    "features = X_names \n",
    "uplift_model.fit(X = df_train[features].values, \n",
    "                 treatment = df_train['treatment_group_key'].values,\n",
    "                 y = df_train[y_name].values)\n",
    "y_preds = uplift_model.predict(df_test[features].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select top N features based on KL filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:48:04.466886Z",
     "start_time": "2021-11-29T22:48:04.461249Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    x51_uplift_increase\n",
      "0    x57_uplift_increase\n",
      "0         x9_informative\n",
      "0    x54_uplift_increase\n",
      "0    x58_uplift_increase\n",
      "0    x52_uplift_increase\n",
      "0    x55_uplift_increase\n",
      "0         x23_irrelevant\n",
      "0       x59_increase_mix\n",
      "0         x21_irrelevant\n",
      "Name: feature, dtype: object\n"
     ]
    }
   ],
   "source": [
    "top_n = 10\n",
    "top_10_features = kl_imp['feature'][:top_n]\n",
    "print(top_10_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:48:04.472658Z",
     "start_time": "2021-11-29T22:48:04.469577Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    x51_uplift_increase\n",
      "0    x57_uplift_increase\n",
      "0         x9_informative\n",
      "0    x54_uplift_increase\n",
      "0    x58_uplift_increase\n",
      "0    x52_uplift_increase\n",
      "0    x55_uplift_increase\n",
      "0         x23_irrelevant\n",
      "0       x59_increase_mix\n",
      "0         x21_irrelevant\n",
      "0         x15_irrelevant\n",
      "0         x11_irrelevant\n",
      "0         x46_irrelevant\n",
      "0         x39_irrelevant\n",
      "0    x53_uplift_increase\n",
      "Name: feature, dtype: object\n"
     ]
    }
   ],
   "source": [
    "top_n = 15\n",
    "top_15_features = kl_imp['feature'][:top_n]\n",
    "print(top_15_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:48:04.477660Z",
     "start_time": "2021-11-29T22:48:04.474508Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    x51_uplift_increase\n",
      "0    x57_uplift_increase\n",
      "0         x9_informative\n",
      "0    x54_uplift_increase\n",
      "0    x58_uplift_increase\n",
      "0    x52_uplift_increase\n",
      "0    x55_uplift_increase\n",
      "0         x23_irrelevant\n",
      "0       x59_increase_mix\n",
      "0         x21_irrelevant\n",
      "0         x15_irrelevant\n",
      "0         x11_irrelevant\n",
      "0         x46_irrelevant\n",
      "0         x39_irrelevant\n",
      "0    x53_uplift_increase\n",
      "0        x10_informative\n",
      "0         x2_informative\n",
      "0         x31_irrelevant\n",
      "0         x19_irrelevant\n",
      "0         x40_irrelevant\n",
      "Name: feature, dtype: object\n"
     ]
    }
   ],
   "source": [
    "top_n = 20\n",
    "top_20_features = kl_imp['feature'][:top_n]\n",
    "print(top_20_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train the Uplift model again with top N features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:49:14.380345Z",
     "start_time": "2021-11-29T22:48:04.483465Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# using top 10 features\n",
    "features = top_10_features \n",
    "\n",
    "uplift_model.fit(X = df_train[features].values, \n",
    "                 treatment = df_train['treatment_group_key'].values,\n",
    "                 y = df_train[y_name].values)\n",
    "y_preds_t10 = uplift_model.predict(df_test[features].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:50:37.407711Z",
     "start_time": "2021-11-29T22:49:14.383261Z"
    }
   },
   "outputs": [],
   "source": [
    "# using top 15 features\n",
    "features = top_15_features \n",
    "\n",
    "uplift_model.fit(X = df_train[features].values, \n",
    "                 treatment = df_train['treatment_group_key'].values,\n",
    "                 y = df_train[y_name].values)\n",
    "y_preds_t15 = uplift_model.predict(df_test[features].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:52:02.337981Z",
     "start_time": "2021-11-29T22:50:37.409572Z"
    }
   },
   "outputs": [],
   "source": [
    "# using top 20 features\n",
    "features = top_20_features\n",
    "\n",
    "uplift_model.fit(X = df_train[features].values, \n",
    "                 treatment = df_train['treatment_group_key'].values,\n",
    "                 y = df_train[y_name].values)\n",
    "y_preds_t20 = uplift_model.predict(df_test[features].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print results for Uplift model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:52:04.153825Z",
     "start_time": "2021-11-29T22:52:02.340008Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_preds = pd.DataFrame([y_preds.ravel(), \n",
    "                         y_preds_t10.ravel(),\n",
    "                         y_preds_t15.ravel(),\n",
    "                         y_preds_t20.ravel(),\n",
    "                         treatments,\n",
    "                         df_test[y_name].ravel()],\n",
    "                        index=['All', 'Top 10', 'Top 15', 'Top 20', 'is_treated', y_name]).T\n",
    "\n",
    "plot_gain(df_preds, outcome_col=y_name, treatment_col='is_treated')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:52:04.355368Z",
     "start_time": "2021-11-29T22:52:04.155514Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "All       0.883113\n",
       "Top 10    0.902750\n",
       "Top 15    0.903005\n",
       "Top 20    0.894030\n",
       "Random    0.493027\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auuc_score(df_preds, outcome_col=y_name, treatment_col='is_treated')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R Learner as base and feed in Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T22:52:04.359998Z",
     "start_time": "2021-11-29T22:52:04.356729Z"
    }
   },
   "outputs": [],
   "source": [
    "r_rf_learner = BaseRRegressor(\n",
    "    RandomForestRegressor(\n",
    "        n_estimators = 100,\n",
    "        max_depth = 8,\n",
    "        min_samples_leaf = 100\n",
    "    ), \n",
    "control_name='control') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T23:07:56.770197Z",
     "start_time": "2021-11-29T22:52:04.361299Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:causalml:Generating propensity score\n",
      "INFO:causalml:Calibrating propensity scores.\n",
      "INFO:causalml:generating out-of-fold CV outcome estimates\n",
      "INFO:causalml:training the treatment effect model for treatment1 with R-loss\n"
     ]
    }
   ],
   "source": [
    "# using all features\n",
    "features = X_names \n",
    "r_rf_learner.fit(X = df_train[features].values, \n",
    "                 treatment = df_train['treatment_group_key'].values,\n",
    "                 y = df_train[y_name].values)\n",
    "y_preds = r_rf_learner.predict(df_test[features].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T23:10:22.727884Z",
     "start_time": "2021-11-29T23:07:56.771842Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:causalml:Generating propensity score\n",
      "INFO:causalml:Calibrating propensity scores.\n",
      "INFO:causalml:generating out-of-fold CV outcome estimates\n",
      "INFO:causalml:training the treatment effect model for treatment1 with R-loss\n"
     ]
    }
   ],
   "source": [
    "# using top 10 features\n",
    "features = top_10_features \n",
    "r_rf_learner.fit(X = df_train[features].values, \n",
    "                 treatment = df_train['treatment_group_key'].values,\n",
    "                 y = df_train[y_name].values)\n",
    "y_preds_t10 = r_rf_learner.predict(df_test[features].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T23:14:02.389101Z",
     "start_time": "2021-11-29T23:10:22.729730Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:causalml:Generating propensity score\n",
      "INFO:causalml:Calibrating propensity scores.\n",
      "INFO:causalml:generating out-of-fold CV outcome estimates\n",
      "INFO:causalml:training the treatment effect model for treatment1 with R-loss\n"
     ]
    }
   ],
   "source": [
    "# using top 15 features\n",
    "features = top_15_features \n",
    "r_rf_learner.fit(X = df_train[features].values, \n",
    "                 treatment = df_train['treatment_group_key'].values,\n",
    "                 y = df_train[y_name].values)\n",
    "y_preds_t15 = r_rf_learner.predict(df_test[features].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T23:18:54.190818Z",
     "start_time": "2021-11-29T23:14:02.390709Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:causalml:Generating propensity score\n",
      "INFO:causalml:Calibrating propensity scores.\n",
      "INFO:causalml:generating out-of-fold CV outcome estimates\n",
      "INFO:causalml:training the treatment effect model for treatment1 with R-loss\n"
     ]
    }
   ],
   "source": [
    "# using top 20 features\n",
    "features = top_20_features \n",
    "r_rf_learner.fit(X = df_train[features].values, \n",
    "                 treatment = df_train['treatment_group_key'].values,\n",
    "                 y = df_train[y_name].values)\n",
    "y_preds_t20 = r_rf_learner.predict(df_test[features].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print results for R Learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T23:18:56.044272Z",
     "start_time": "2021-11-29T23:18:54.192388Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_preds = pd.DataFrame([y_preds.ravel(), \n",
    "                         y_preds_t10.ravel(),\n",
    "                         y_preds_t15.ravel(),\n",
    "                         y_preds_t20.ravel(),\n",
    "                         treatments,\n",
    "                         df_test[y_name].ravel()],\n",
    "                        index=['All', 'Top 10', 'Top 15', 'Top 20', 'is_treated', y_name]).T\n",
    "\n",
    "plot_gain(df_preds, outcome_col=y_name, treatment_col='is_treated')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T23:18:56.211023Z",
     "start_time": "2021-11-29T23:18:56.045507Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "All       0.898363\n",
       "Top 10    0.906834\n",
       "Top 15    0.897248\n",
       "Top 20    0.898664\n",
       "Random    0.493027\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print out AUUC score\n",
    "auuc_score(df_preds, outcome_col=y_name, treatment_col='is_treated')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(a relatively smaller enhancement on the AUUC is observed in this R Learner case)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### S Learner as base and feed in Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T23:18:56.214863Z",
     "start_time": "2021-11-29T23:18:56.212530Z"
    }
   },
   "outputs": [],
   "source": [
    "slearner_rf = BaseSRegressor(\n",
    "    RandomForestRegressor(\n",
    "        n_estimators = 100,\n",
    "        max_depth = 8,\n",
    "        min_samples_leaf = 100\n",
    "    ), \n",
    "    control_name='control')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T23:26:06.103012Z",
     "start_time": "2021-11-29T23:18:56.216262Z"
    }
   },
   "outputs": [],
   "source": [
    "# using all features\n",
    "features = X_names \n",
    "slearner_rf.fit(X = df_train[features].values, \n",
    "                treatment = df_train['treatment_group_key'].values,\n",
    "                y = df_train[y_name].values)\n",
    "y_preds = slearner_rf.predict(df_test[features].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T23:27:14.666207Z",
     "start_time": "2021-11-29T23:26:06.104715Z"
    }
   },
   "outputs": [],
   "source": [
    "# using top 10 features\n",
    "features = top_10_features \n",
    "slearner_rf.fit(X = df_train[features].values, \n",
    "                treatment = df_train['treatment_group_key'].values,\n",
    "                y = df_train[y_name].values)\n",
    "y_preds_t10 = slearner_rf.predict(df_test[features].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T23:28:56.526315Z",
     "start_time": "2021-11-29T23:27:14.667927Z"
    }
   },
   "outputs": [],
   "source": [
    "# using top 15 features\n",
    "features = top_15_features \n",
    "slearner_rf.fit(X = df_train[features].values, \n",
    "                treatment = df_train['treatment_group_key'].values,\n",
    "                y = df_train[y_name].values)\n",
    "y_preds_t15 = slearner_rf.predict(df_test[features].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T23:31:13.640303Z",
     "start_time": "2021-11-29T23:28:56.527765Z"
    }
   },
   "outputs": [],
   "source": [
    "# using top 20 features\n",
    "features = top_20_features \n",
    "slearner_rf.fit(X = df_train[features].values, \n",
    "                treatment = df_train['treatment_group_key'].values,\n",
    "                y = df_train[y_name].values)\n",
    "y_preds_t20 = slearner_rf.predict(df_test[features].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print results for S Learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T23:31:15.343770Z",
     "start_time": "2021-11-29T23:31:13.641802Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_preds = pd.DataFrame([y_preds.ravel(), \n",
    "                         y_preds_t10.ravel(),\n",
    "                         y_preds_t15.ravel(),\n",
    "                         y_preds_t20.ravel(),\n",
    "                         treatments,\n",
    "                         df_test[y_name].ravel()],\n",
    "                        index=['All', 'Top 10', 'Top 15', 'Top 20', 'is_treated', y_name]).T\n",
    "\n",
    "plot_gain(df_preds, outcome_col=y_name, treatment_col='is_treated')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-29T23:31:15.523386Z",
     "start_time": "2021-11-29T23:31:15.346076Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "All       0.866137\n",
       "Top 10    0.882310\n",
       "Top 15    0.882737\n",
       "Top 20    0.871582\n",
       "Random    0.493027\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print out AUUC score\n",
    "auuc_score(df_preds, outcome_col=y_name, treatment_col='is_treated')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we demonstrated how our Filter method functions are able to select important features and enhance the AUUC performance (while the results might vary among different datasets, models and hyper-parameters)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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